计量学报2024，Vol.45Issue(2)：285-293,9.DOI:10.3969/j.issn.1000-1158.2024.02.20
基于特征优化和BSO-RBF神经网络的NOx浓度预测模型
NOx Concentration Prediction Model Based on Feature Optimization and BSO-RBF Neural Network
摘要
Abstract
In the process of thermal power generation,the operation condition of combustion system is complicated and the delay is large,which makes it difficult to accurately measure the inlet NOx mass concentration in the selective catalytic reduction(SCR)flue gas denitration system.To solve this problem,a prediction model based on feature optimization and radial basis(RBF)neural network is proposed.Firstly,the variable after feature optimization is taken as the final input variable of the model.Secondly,the beetle swarm optimization(BSO)is used to optimize the neural network hyperparameters.Finally,a prediction model of inlet NOx concentration is established.The results show that the predictive results of the optimized variables are better than those of the original variables.After feature optimization and timely delay,the SRMSE of the model decreased by 44.5％,and the R2 increased by 2.3％.The neural network hyperparameters determined by BSO also improved the accuracy of the model.关键词
NO,浓度预测/特征优化/天牛群优化算法/径向基函数/神经网络Key words
NOx concentration prediction/feature optimization/beetle swarm optimization algorithm/RBF/neural network分类
通用工业技术引用本文复制引用
张国兴,王世朋..基于特征优化和BSO-RBF神经网络的NOx浓度预测模型[J].计量学报,2024,45(2):285-293,9.基金项目
国家重点研发计划(2018YFB0604300) （2018YFB0604300）
